1 Plots

  • Hadley Wickham: grammar of graphics

  • Hans Rosling: GapMinder

Gapminder World - Wealth & Health of Nations

1.1 Static: ggplot

1.1.1 Scatterplot

suppressPackageStartupMessages({
  library(dplyr)
  library(ggplot2)
  library(gapminder)
})

# preview data
gapminder
## Source: local data frame [1,704 x 6]
## 
##        country continent  year lifeExp      pop gdpPercap
##         (fctr)    (fctr) (int)   (dbl)    (int)     (dbl)
## 1  Afghanistan      Asia  1952  28.801  8425333  779.4453
## 2  Afghanistan      Asia  1957  30.332  9240934  820.8530
## 3  Afghanistan      Asia  1962  31.997 10267083  853.1007
## 4  Afghanistan      Asia  1967  34.020 11537966  836.1971
## 5  Afghanistan      Asia  1972  36.088 13079460  739.9811
## 6  Afghanistan      Asia  1977  38.438 14880372  786.1134
## 7  Afghanistan      Asia  1982  39.854 12881816  978.0114
## 8  Afghanistan      Asia  1987  40.822 13867957  852.3959
## 9  Afghanistan      Asia  1992  41.674 16317921  649.3414
## 10 Afghanistan      Asia  1997  41.763 22227415  635.3414
## ..         ...       ...   ...     ...      ...       ...
# get range of available data
summary(gapminder)
##         country        continent        year         lifeExp     
##  Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
##  Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
##  Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
##  Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
##  Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
##  Australia  :  12                  Max.   :2007   Max.   :82.60  
##  (Other)    :1632                                                
##       pop              gdpPercap       
##  Min.   :6.001e+04   Min.   :   241.2  
##  1st Qu.:2.794e+06   1st Qu.:  1202.1  
##  Median :7.024e+06   Median :  3531.8  
##  Mean   :2.960e+07   Mean   :  7215.3  
##  3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
##  Max.   :1.319e+09   Max.   :113523.1  
## 
# setup dataframe
g = gapminder %>%
  filter(year==2007) %>%   # most recent year 
  mutate(pop_m = pop/1e6)  # population, millions

# plot scatterplot of most recent year 
s = ggplot(g, aes(x=gdpPercap, y=lifeExp)) +
  geom_point()
s

# add aesthetic of size by population
s = s + 
  aes(size=pop_m)
s

# add aesthetic of color by continent
s = s + 
  aes(color=continent)
s

# add title, update axes labels
s = s + 
  ggtitle('Health & Wealth of Nations for 2007') +
  xlab('GDP per capita ($/year)') +
  ylab('Life expectancy (years)')
s

# label legend
s = s + 
  scale_colour_discrete(name='Continent') +
  scale_size_continuous(name='Population (M)')
s

Your Turn

Now with country emissions datasets…

1.1.2 Boxplot

# boxplot by continent
b = ggplot(g, aes(x=continent, y=lifeExp)) +
  geom_boxplot()
b

# match color to continents, like scatterplot
b = b +
  aes(fill=continent)
b

# drop legend, add title, update axes labels
b = b +
  theme(legend.position='none') +
  ggtitle('Life Expectancy by Continent for 2007') +
  xlab('Continent') +
  ylab('Life expectancy (years)')
b

Your Turn: Make a similar plot but for gdpPercap. Be sure to update the plot’s aesthetic, axis label and title accordingly.

1.2 Interactive: plotly

ggplot2 | plotly

suppressPackageStartupMessages({
  library(plotly) # install.packages('plotly')
})

# scatterplot (Note: key=country shows up on rollover)
s = ggplot(g, aes(x=gdpPercap, y=lifeExp, key=country)) +
  geom_point()
ggplotly(s)

# boxplot
ggplotly(b)

Your Turn: Expand the interactive scatterplot to include all the other bells and whistles of the previous plot in one continuous set of code (no in between setting of s).

1.3 Interactive: Exploding Boxplot

library(explodingboxplotR) # devtools::install_github('timelyportfolio/explodingboxplotR')

exploding_boxplot(g,
  y = 'lifeExp',
  group = 'continent',
  color = 'continent',
  label = 'country')

1.4 Interactive: Motion Plot

The googleVis package ports most of the Google charts functionality.

For every R chunk must set option results='asis', and once before any googleVis plots, set op <- options(gvis.plot.tag='chart').

suppressPackageStartupMessages({
  library(googleVis) # install.packages('googleVis')
})
op <- options(gvis.plot.tag='chart')

m = gvisMotionChart(
  gapminder, 
  idvar='country', 
  timevar='year', 
  xvar='gdpPercap', 
  yvar='lifeExp', 
  colorvar='continent',
  sizevar='pop')
plot(m)

2 Maps: tmap

Thematic maps

2.1 Static

library(tmap) # install.packages('tmap')

data(World, metro)
metro$growth <- (metro$pop2020 - metro$pop2010) / (metro$pop2010 * 10) * 100

m <- tm_shape(World) +
    tm_polygons("income_grp", palette="-Blues", contrast=.7, id="name", title="Income group") +
    tm_shape(metro) +
    tm_bubbles("pop2010", col = "growth", 
               border.col = "black", border.alpha = .5, 
               style="fixed", breaks=c(-Inf, seq(0, 6, by=2), Inf),
               palette="-RdYlBu", contrast=1, 
               title.size="Metro population", 
               title.col="Growth rate (%)", id="name") + 
    tm_style_gray() + tm_format_World()
m

2.2 Interactive

tmap_leaflet(m)